Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks

The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale

Direct control strategy for a four-level three-phase flying-capacitor inverter

A direct predictive control strategy is proposed for a three-phase four-level flying-capacitor (FC) inverter in this paper. The balancing of the FC voltages, a challenge in applications with small capacitors and low switching frequencies, is done without any modulation, simply using tables calculated offline. These allow the realization of fast-dynamics output currents with reduced dv/dt in the output voltages and reduced switching frequencies. Moreover, no interharmonics are created when operating at low switching frequencies and with reference currents containing multiple harmonic components, which is a key feature for active power filters. Simulations and experimental results are presented to demonstrate the excellent performance of the direct control strategy in comparison with a conventional pulsewidth-modulation control technique, mostly for operation at low switching frequencies

Mathematical control of complex systems 2013

Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)